app.py

# coding=utf8
from gpt_index import SimpleDirectoryReader, GPTListIndex, GPTSimpleVectorIndex, LLMPredictor, PromptHelper
from langchain import OpenAI
import gradio as gr
import random
import time
import sys
import os
from transformers import pipeline
p = pipeline("automatic-speech-recognition")


os.environ["OPENAI_API_KEY"] = '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'

md = """This is some code:

hello

```py
def fn(x, y, z):
    print(x, y, z)
"""
def transcribe(audio):
    text = p(audio)["text"]
    return text
def construct_index(directory_path):
    max_input_size = 10000
    num_outputs = 10000
    max_chunk_overlap = 20000
    chunk_size_limit = 600000

    prompt_helper = PromptHelper(max_input_size, num_outputs, max_chunk_overlap, chunk_size_limit=chunk_size_limit)

    llm_predictor = LLMPredictor(llm=OpenAI(temperature=0.0, model_name="text-davinci-003", max_tokens=num_outputs))

    documents = SimpleDirectoryReader(directory_path).load_data()

    index = GPTSimpleVectorIndex.from_documents(documents)
    index.save_to_disk('index.json')

    return index


def chatbot(input_text):

    index = GPTSimpleVectorIndex.load_from_disk('index.json')
    response = index.query(input_text)
    return str(response.response)


with gr.Blocks() as demo:
    gpt = gr.Chatbot(label="Zoh", elem_id="chatbot").style(height=800)
    msg = gr.Textbox( show_label=False,
                placeholder="Bem vindo ao Hippo Supermercados, em que posso ajuda-lo?",
            ).style(container=False)
    clear = gr.Button("Limpar Conversa")
    gr.Audio(source="microphone", type="filepath",label="ESTÁ COM DIFICULDADES EM ESCREVER? CLIQUE E ME DIGA O QUE DESEJA")
    def respond(message, chat_history):
        chat_history.append((message, chatbot(message)))
        time.sleep(1)
        vetor = []
        realPath = str(os.path.dirname(os.path.realpath(__file__)))

        if str(message).upper()=="OLA" or str(message).upper()=="OLÁ" or str(message).upper()=="OI":
            vetor = vetor + [((realPath + "\\images\\hippo-apresentacao.mp4",), "")]
        elif str(message).upper() == "VINHO CASA DEL RONCO PINOT GRIGIO" :
                vetor = vetor + [((realPath + "\\images\\casa-del-ronco-branco.png",), "")]
        elif str(message).upper() == "VINHO SURVIVOR CHENIN BLANC" :
                vetor = vetor + [((realPath + "\\images\\survivor-branco.png",), "")]
        elif str(message).upper() == "VINHO PORTO NOVA VERDE" :
                vetor = vetor + [((realPath + "\\images\\porta-nova-branco.jpg",), "")]
        elif str(message).upper() == "VINHO QUINTA DO PINTO ARINTO BRANCO" :
                vetor = vetor + [((realPath + "\\images\\quinta-pinto-arinto-branco.png",), "")]
        elif str(message).upper() == "VINHO 1492 CHARDONNAY" :
                vetor = vetor + [((realPath + "\\images\\chardonay-branco.jpg",), "")]
        elif str(message).upper() == "ME SUGIRA UM VINHO TINTO BOM COM QUEIJO" :
                vetor = vetor + [((realPath + "\\images\\TNT-CABERNET.png",), "")]
        elif str(message).upper() == "VINHO BOM COM CHOCOLATE" :
                vetor = vetor + [((realPath + "\\images\\TNT-CABERNET.png",), "")]
        elif str(message).upper() == "VINHO BOM COM PEIXE" :
                vetor = vetor + [((realPath + "\\images\\luson-branco.png",), "")]
        elif str(message).upper() == "VINHAS DO LASSO COLHEITA SELECIONADA" :
                vetor = vetor + [((realPath + "\\images\\lasso-colheita-rose.png",), "")]
        elif str(message).upper() == "DOM CAMPOS MOSCATEL" :
                vetor = vetor + [((realPath + "\\images\\dom-campos-rose.png",), "")]
        elif str(message).upper() == "BECAS ROSE MEIO SECO" :
                vetor = vetor + [((realPath + "\\images\\becas-rose.png",), "")]
        elif str(message).upper() == "PORTA DA RAVESSA" :
                vetor = vetor + [((realPath + "\\images\\luson-branco.png",), "")]
        


        return "", chat_history+vetor

    clear.click(lambda:None, None, gpt, queue=False,)
    msg.submit(respond, [msg, gpt], [msg,gpt])

index = construct_index("docs")
demo.launch()